The rise of the cost-benefit robots

And so the insurrection is beginning. Last week Japanese insurance Fukoku Mutual Life Insurance announced that it was going to be replacing 34 staff with an artificial intelligence that would be calculating payouts (although, it noted, with human oversight still making final approvals).

The technology would improve productivity by 30% and the firm expected to save some 140m Yen a year (around £1m) after the 200m Yen investment.

Now I’m sure that this implementation of IBM’s Watson technology (remember: Watson was the man who predicted in 1943 a global market for maybe five computers) will be very whizzy. But excuse me whilst I contend that Fukoku’s PR make this AI sound like every IT business case I’ve ever seen: cost savings through headcount reduction blah blah, productivity gains blah blah. I also know how so many of those technology business cases turn out. Somewhat disappointingly.

AI is a emerging technology. Investing in it to streamline existing business processes is daft. A three year ROI? On a still emerging field of technology? In a sector that is reknowned for its risk aversion?

Based on the numbers that have been PRed, a 30% increase in staff numbers would have cost the company 47m Yen. A quarter of the cost of the Watson investment, and with presumably far lower risk (but, naturally, no PRTech points.

Let’s be clear. I’m not disputing the potential for AI and Machine Learning technologies. But what I do struggle with is the idea that early implementations can be done in a culture of predictable business case planning to achieve back-office cost savings. The chances of it going wrong just don’t (in my view) justify the small potential benefits. Far better to think about how such technologies can be used as an investment to grow a business rather than just incrementally improve. How could AI be used to create entirely new classes of insurance product, unimaginable in the contraints of human working?

A nice benchmark for this kind of adoption of technology that drives new business models is the London Congestion Charge. It’s a novel form of taxation that would have been unimaginable in the days before automatic number plate recognition. Now that that technology has been proven, it’s moving back into cost-saving and efficiency-type applications replacing toll-booths on roads where they were an OK solution.

Emerging technologies are just that. They don’t have associated good practice. Implementing them in traditional IT models of investment case planning runs risk in two ways – firstly that the business cases are even more fictional than the average, and secondly (and far more importantly) by framing the project as one with knowable outcomes, the opportunity to learn and to pivot becomes much diminished. They’ve laid the staff off. They’ve set the productivity and cost saving targets. They’ll spend their time trying (possibly unsuccessfully) to hit those targets. And they’ll have missed so much about the technology they’re experimenting with as a result because they’ve already determined “the answer”.